Amid the Church abuse allegations, we set out to understand how Catholics in New York City have grappled with their faith, and, in doing so, offer a window into Catholicism in one of the world's most diverse cities, where 1 in 3 residents are Catholic:
“Shaun said, ‘You’re helping them fight me. You’re helping them raise money by collecting at church.'"
"If anything, it makes it more urgent for me to stay, not to abandon this church and the people who are in it because we need to heal together."
"Saying that I’m not a Catholic is a huge statement. The fact that I was raised something, and I’m deciding whether or not I am still that thing.”
Mr. McGarvey said he was 16 when he was first sexually assaulted by a priest.
“I hate saying his name,” said Mr. McGarvey, now 52.
But he recently stopped going to church.
After coming out, Mr. Sánchez said he initially convinced himself that the church “didn’t hate me for being gay.”
“We are broken people, and so it doesn’t shock me that this has taken place. What shocks me more is sort of the response to it."
"You can show everyone that this is not going to stop us,” Ms. Jimenez said. “And that, if anything, we're going to do even more.”
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Do Share the above tweet 👆
These are going to be very simple yet effective pure price action based scanners, no fancy indicators nothing - hope you liked it.
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52 Week High
One of the classic scanners very you will get strong stocks to Bet on.
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This scanner will give you short term bet breakouts like hourly or 2Hr breakout
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How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)